Amy Zhang

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 14 papers; trusts 597 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 412 citations into it; #4,496 of 35,345 by trust here, #4,489 by citations
• Reinforcement Learning — 262 citations into it; #1,440 of 2,242 by trust here, #1,586 by citations
• Computer Vision and Pattern Recognition — 149 citations into it
• Representation & Self-Supervised Learning — 83 citations into it
• Control and Systems Engineering — 50 citations into it
• Computational Theory and Mathematics — 44 citations into it; #4,284 of 12,182 by trust here, #5,465 by citations
• Management Science and Operations Research — 44 citations into it; #3,638 of 11,946 by trust here, #3,825 by citations
• Evaluation & Benchmarks — 30 citations into it

Whom Amy Zhang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Kaiming He
7. Sergey Levine
8. Yann LeCun
9. Volodymyr Mnih
10. Ronald J. Williams

Who trusts Amy Zhang overall

1. Shagun Sodhani
2. Roberta Raileanu
3. Sergey Levine
4. Georgios Papoudakis
5. Cameron S. Allen
6. Ofir Nachum
7. Joelle Pineau
8. Rishabh Agarwal
9. Yilun Du
10. Benjamin Eysenbach

Papers from Amy Zhang's perspective

Ordered by how strongly the researchers Amy Zhang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Composable Planning with Attributes (2018) — 68 citations
• A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning (2018) — 200 citations
• Improving Sample Efficiency in Model-Free Reinforcement Learning from Images (2019) — 518 citations
• Learning Invariant Representations for Reinforcement Learning without Reconstruction (2020) — 593 citations
• VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training (2022) — 495 citations
• Invariant Causal Prediction for Block MDPs (2020) — 159 citations
• Decoupling Dynamics and Reward for Transfer Learning (2018) — 80 citations
• Multi-Task Reinforcement Learning with Context-based Representations (2021) — 259 citations
• Out-of-Distribution Generalization via Risk Extrapolation (REx) (2020) — 1,228 citations
• Learning Causal State Representations of Partially Observable Environments (2019) — 60 citations
• Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL (2021) — 13 citations
• Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning (2022)
• Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning (2022) — 47 citations
• Block Contextual MDPs for Continual Learning (2021) — 34 citations